In current computing ecosystems, data packets are transmitted between computing devices and/or device components at a rapid pace. Depending on the specific purpose of each received data packet, the receiving computing device processes the data packet in a certain way. Accordingly, the received data packets are categorized or otherwise classified according to “flows” that define operations and/or other rules for the processing of the received packets. As speed is a critical feature in computing systems, a variety of mechanisms have been employed to increase the speed at which such packet flow classifications occur.
Hash tables are often used to improve the speed of data lookup operations when it is necessary to sift through large amounts of data to identify a match. In particular, hash tables (e.g. associative arrays) utilize hash functions to deterministically map data of variable length to data of fixed size (i.e., the signature). Further, the particular hash function may be selected to uniformly distribute the data across intervals of equal size (i.e., hash buckets) based on the value of the signature generated by the hash function. Thereafter, when attempting to identify a match during a data lookup operation, one must only compare the data to the data stored in the interval having the same signature (i.e., not all of the data in the table/list). In that way, hash tables optimize the performance of data lookup operations by minimizing the amount of data that has to be compared against the input data.